OK, he wasn’t talking specifically about automating tests. But he talk about automating the jobs that can be automated and redirecting the human effort towards things “where human empathy and understanding are still very, very unique.”

His topic was taxing the output of robots and using those funds to train the displaced workers towards those new roles.

“So if you can take the labor that used to do the thing automation replaces, and financially and training-wise and fulfillment-wise have that person go off and do these other things, then you’re net ahead.”

Read the full article, and see the video here. 

My wife and I recently had a conversation which I need to work into my upcoming talk on metrics.  I’m traveling and she is planning to pick me up from the airport. Its a long flight.  She is wondering about flight updates and what time she should show at the airport.

I tell her that I’ll text her if the plane takes off on time, or is delayed.  She initially says that she needs to know if it will arrive on time, not if it departs on time.  Then, immediately realizes that if the plane departs late, it surely will arrive late. So, having that bit of information regarding on time departure does have some value.

Sometimes we let perfect get in the way of progress. A metric can be gamed, or its not comprehensive, or it doesn’t tell the whole story. However, if you understand the underlying process, having half the information may provide some useful information indeed.

I have a talk at the upcoming STPCON in Phoenix, called Metrics: Choose Wisely.  I’ll be featuring some of the content here as a preview.  Please feel free to comment, ask questions here, and by all means, do attend the conference if you can.

At the talk, I’ll be providing a methodology for creating software quality metrics that tie into your business goals. Then, will pick apart some of my work by showing various fallacies with using these metrics.  The first fallacy to watch for is survivor bias.

For a quick exercise, think about a medieval castle.  What material are castles made of?

Edinburgh Castle – illustrating that our conception of castles are made of stone.

Yeah, stone castles are what we think about when we think about castles. In fact, most castles were made of timbers – out of wood.  However, today we mostly see the castles that survived for hundreds of years. We just see the stone castles because the wooden castles have burned or rotted away.

For an example where survivor bias may impact conclusions on a metric, consider this chart which shows the priority of open bugs.

Open bugs by priority

Someone may draw the conclusion that quality is pretty good here.  The only 1% of the bugs are of the highest priority, and the distribution looks normal.  However, the underlying data is only of the bugs that are still open.  This team may, or may not, deliver software that has many high priority bugs, but they fix those bugs quickly. We should look at a distribution for all of the bugs, not just the open bugs.

Another example where survivor bias exists is with customer satisfaction surveys.  Getting a sense of quality from your customers is vital, but you have to remember that the survey results that you see are the results from the people that completed your survey.  The survivors.  You don’t see the results from people who gave up on the survey.  This is why I like to use very short surveys, like the Net-Promoter Score.  The shorter the survey, generally the more survivors you have.